Grasping Representation: Since we are the first method to regress 6-DoF grasping poses from monocular data alone, we first introduce our novel representation, better suited for our setup. In the core, we combine and extend the different representations from Contact-GraspNet and L2G to derive our new formulation (TABLE REF ).
Contact-GraspNet [1]} uses the visible grasping point \(P_1\) , grasping axis \(\mathbf {n_x}\) , approaching axis \(\mathbf {n_z}\)  and grasping width \(w\)  to represent the 6-DoF grasp, whereas L2G [2]} relies on two 3D grasping keypoints \(P_1, P_2\)  together with the angle between gripper plane and platform plane \(\phi \) . As recovering sensitive 3D information from 2D input is a difficult ill-posed problem, the pattern we propose in this work is to leverage 2D features to a large extent. Specifically, we split \(P_1\)  into a 2d keypoint \(p\)  aligned with planar grasping represented by [3]} and a further depth value \(d\) , ending up with \(\lbrace \mathbf {n_x}, p, d, w , \phi \rbrace \)  as our final parameterization, as shown in Fig. REF  (see Sec. REF for 6-DoF pose recovery).
<TABLE>